ABSTRACT A fundamental goal of human genetics is to decipher the relationship between genotype and phenotype. Cancer is defined as a disease comprising a heritable genetic component that confers cancer predisposition and an acquired (somatic) component where disease is driven by an accumulation of genetic mutations leading to ever increasing deregulation of normal cellular functions. Population based genome wide association studies (GWAS) and whole genome sequencing (WGS) analyses have identified thousands of germline risk variants for ovarian cancer and somatic non-coding mutations involved in ovarian cancer development. Identifying genomic regions where there are interactions between germline and somatic variants may enable us to identify the critical drivers of disease. We have established an end-to-end pipeline that can efficiently evaluate the functional significance of thousands of genetic variants in disease at once. We have also established ex-vivo models of fallopian tube secretary epithelial cells (precursors of ovarian cancer) and in vitro 3D models of chemoresistant ovarian cancer. In this proposal, we plan to address provocative question #3 ?Do genetic interactions between germline variations and somatic mutations contribute to differences in tumor evolution or response to therapy?? with the following specific aims: (1) Use computational approaches, to identify genomic regions where germline and somatic genetic variants converge to indicate shared target genes and regulatory networks driving ovarian cancer development; (2) Use chromosome conformation capture assays to validate interactions between regulatory targets and their target genes; (3) Use CRISPR/Cas9 screens to establish the functional significance of germline-somatic interacting regions in ovarian cancer development.